MALWARE IMAGE PREDICTION AND CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK

Authors

  • Dr. P. Ramya M.Tech., Ph.D., Associate Professor, Department of Computer Science and Engineering, Mahendra Engineering College, Namakkal
  • Manikandan S UG Student, Department of Computer Science and Engineering, Mahendra Engineering College, Namakkal
  • Naveen G R UG Student, Department of Computer Science and Engineering, Mahendra Engineering College, Namakkal
  • Madanbabu S UG Student, Department of Computer Science and Engineering, Mahendra Engineering College, Namakkal
  • Madhankumar N UG Students, Department of Computer Science and Engineering, Mahendra Engineering College, Namakkal

DOI:

https://doi.org/10.29121/shodhkosh.v5.i6.2024.2602

Keywords:

Malware Image Classification, Machine Learning, Deep Learning, Features Extraction, Classification

Abstract [English]

Without users' permission, malware software can infect computers or other devices. Through these loopholes, criminals commit a range of illegal and criminal offences that violate the legitimate rights and interests of the nation. Traditional malware categorization techniques fall into two categories: static analysis and dynamic analysis. It is usually not necessary to execute malware binary samples in order to perform static analysis techniques, and disassembly makes it simple to recover important data such as text lists, routines, and hash values. The static analysis methods offer a high accuracy rate and a simple operation with a low consumption time. Static analysis tools, however, are limited to analyzing malware binary samples at the surface level, where they are readily influenced by deformation and other means of confusion. Furthermore, it is challenging to identify and categorize unknown malware. Methods of dynamic analysis are not impacted by obfuscation and can operate in a virtual environment. It has the ability to recognize newly discovered malware samples and track the dynamic alterations of malware binary samples over time. Nevertheless, it's an extremely intricate and time-consuming process.


Malware has become one of the largest security threats in recent years due to its rapid growth. But feature engineering makes it difficult to handle large amounts of malware and readily limits the use of standard machine learning methods for malware categorization. However, dynamic analysis methodologies are not appropriate for efficiently categorizing large amounts of malware due to their complexity and high cost. In light of this, we propose a novel static malware detection method based on the convolutional neural network (CNN) employed in this work. Unlike existing methods, we use the data enhancement method to fix the unbalanced datasets, turn every viral byte into a colour image, and provide a better design.

References

Belal, Mohamad Mulham, and Divya Meena Sundaram. "Global-Local Attention-Based Butterfly Vision Transformer for Visualization-Based Malware Classification." IEEE Access (2023). DOI: https://doi.org/10.1109/ACCESS.2023.3293530

Peppes, Nikolaos, et al. "Malware Image Generation and Detection Method using DCGANs and Transfer Learning." IEEE Access (2023). DOI: https://doi.org/10.1109/ACCESS.2023.3319436

Kim, Jeongwoo, Joon-Young Paik, and Eun-Sun Cho. "Attention-Based Cross-Modal CNN Using Non-Disassembled Files for Malware Classification." IEEE Access 11 (2023): 22889-22903. DOI: https://doi.org/10.1109/ACCESS.2023.3253770

Khan, Faiza Babar, et al. "Detection of data scarce malware using one-shot learning with relation network." IEEE Access (2023). DOI: https://doi.org/10.1109/ACCESS.2023.3293117

Geremias, Jhonatan, et al. "Towards a Reliable Hierarchical Android Malware Detection Through Image-based CNN." 2023 IEEE 20th Consumer Communications & Networking Conference (CCNC). IEEE, 2023. DOI: https://doi.org/10.1109/CCNC51644.2023.10060381

Prajapati, Pratikkumar, and Mark Stamp. "An empirical analysis of image-based learning techniques for malware classification." Malware analysis using artificial intelligence and deep learning (2021): 411-435. DOI: https://doi.org/10.1007/978-3-030-62582-5_16

Singh, Jaiteg, et al. "Classification and analysis of android malware images using feature fusion technique." IEEE Access 9 (2021): 90102-90117. DOI: https://doi.org/10.1109/ACCESS.2021.3090998

Sharma, Gurumayum Akash, Khundrakpam Johnson Singh, and Maisnam Debabrata Singh. "A deep learning approach to image-based malware analysis." Progress in Computing, Analytics and Networking: Proceedings of ICCAN 2019. Singapore: Springer Singapore, 2020. 327-339. DOI: https://doi.org/10.1007/978-981-15-2414-1_33

O’Shaughnessy, Stephen, and Stephen Sheridan. "Image-based malware classification hybrid framework based on space-filling curves." Computers & Security 116 (2022): 102660. DOI: https://doi.org/10.1016/j.cose.2022.102660

Iadarola, Giacomo, et al. "Image-based Malware Family Detection: An Assessment between Feature Extraction and Classification Techniques." IoTBDS. 2020. DOI: https://doi.org/10.5220/0009817804990506

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Published

2024-06-30

How to Cite

Ramya, P., S, M., G R, N., S, M., & N, M. (2024). MALWARE IMAGE PREDICTION AND CLASSIFICATION USING CONVOLUTIONAL NEURAL NETWORK. ShodhKosh: Journal of Visual and Performing Arts, 5(6), 1823–1830. https://doi.org/10.29121/shodhkosh.v5.i6.2024.2602